microbiological water quality at non-human influenced reference beaches in southern california...

ABSTRACT

Although wet weather discharges from urbanwatersheds may have elevated concentrations of fecalindicator bacteria that impact water quality at swim-ming beaches, not all of these bacteria may arise fromhuman sources. In this study, the contribution of fecalindicator bacteria was quantified at coastal referencebeaches in southern California having minimal humanimpact. Operationally, reference beaches weredefined as open beaches with breaking waves thatreceive runoff from undeveloped (>93% open space)watersheds and were selected to represent geographi-cal conditions and watershed sizes. Six referencebeaches were sampled during nine storm events dur-ing the 2004 - 2005 and 2005 - 2006 wet seasons.Samples were analyzed for total coliform, Escherichiacoli (E. coli), and enterococci in the discharge fromthe undeveloped watershed and in the wave washwhere the discharge and surf zone initially mix.Samples collected during wet weather exceededwater quality thresholds established by the State ofCalifornia greater than 10 times more frequently dur-ing wet weather than during recent dry weather insummer or winter, although the frequency differed bybeach. These exceedences were greatest <24 hoursfollowing recorded rainfall, then steadily declined forthe following three days. Early season storms exceed-ed water quality thresholds more than twice as fre-quently as late season storms. In addition, over halfof these early season storms exceeded thresholds formultiple bacterial indicators, while the vast majorityof late season storms only exceeded thresholds for asingle bacterial indicator. Large storms exceededwater quality thresholds three times more frequentlythan smaller-sized storms. This was partly due to thebreaching of sand berms during large storm events;small storms could not breach these berms and thisrestricted watershed discharges from entering the surfzone. When watershed discharges did enter the surfzone, bacterial concentrations in the wave wash werecorrelated with watershed bacterial flux.

INTRODUCTION

Beaches in Southern California are a valuablerecreational resource for swimming, surfing, and otherbody contact activities. For example, greater than 175 million beach-goers visit southern Californiabeaches annually, more than all other parts of thecountry combined (Schiff et al. 2001). This year-round activity results in tremendous economic rev-enue estimated at more than $9 billion annually inocean related activities for the region (NRC 1990).

Fecal indicator bacteria (total coliform, E.coli/fecal coliform, and enterococci) are used tomonitor the water quality of marine beaches becausethey have been shown to correlate with swimmingrelated illness. For example, Cabelli (1982) demon-strated that increases in concentrations of enterococcicorrelated with an increase in the risk of highly cred-ible gastrointestinal illness among swimmers atbeaches in New Jersey. In Santa Monica Bay,California, Haile et al. (1999) observed an increasein the relative risk for diarrhea with blood and highlycredible gastrointestinal illness in swimmers exposedto higher concentrations of enterococci.

While the water quality at most beaches in south-ern California meets water quality thresholds estab-lished by the State during dry weather, several beach-es have impaired water quality based on routine fecalindicator bacteria monitoring. Noble et al. (2000)conducted a regional study of all southern Californiabeaches and found that approximately 5% of theshoreline exceeded water quality thresholds for fecalindicator bacteria during the summer of 1998. Thislevel of exceedence was not randomly distributed.More than half of the exceedences occurred nearstorm drains that discharge across the beach. A retro-spective analysis of fecal indicator bacteria based onfive years of daily beach monitoring during dryweather in Santa Monica Bay found similar results,with over half of the water quality exceedences occur-ring in front of storm drains (Schiff et al. 2002).

Microbiological water quality at non-human impacted reference beaches insouthern California during wet weather

John F. Griffith, Kenneth C. Schiffand Gregory S. Lyon

Microbial water quality at reference beaches - 195

The microbial water quality of beaches in south-ern California drastically changes following rain-storms. Noble et al. (2003) repeated their 1998 sum-mer study, but sampled following a significant rain-fall event during the winter of 1998-1999. In thiscase, over half of all beaches exceeded fecal indica-tor bacteria water quality thresholds. This frequencyof impaired water quality jumped to nearly 90%when these beaches were located in front of stormdrains. Similarly, Schiff et al. (2002) observed adoubling of microbial water quality exceedencesbetween dry and wet weather, even though wetweather represented less than 10% of the year.

There are many sources of bacteria that couldpotentially be found in storm drains that discharge tobeaches. Some of these sources may be of humanorigin including sewage spills, leaking sanitarysewage systems, faulty septic systems, or illicit dis-charges and illegal dumping (Geldrich 1978).However, many bacteria may actually arise from nat-ural sources. Fecal indicator bacteria such as totalcoliform, E. coli/fecal coliform and enterococci are acomponent of the gut microflora of all warm-blood-ed animals, including domesticated dogs and cats,and wild birds and mammals (Grant et al. 2001,Fujioka 1995). Furthermore, fecal indicator bacteriamay have extended survival or even regrow in beachsediments and wrack (Valiela et al. 1991, Weiskel etal. 1996, Demaris et al. 2002, City of SanDiego/MEC Weston 2004, Anderson et al. 2005).Therefore, the reference condition for bacterial waterquality, including those beaches that are located atthe mouth of undeveloped watersheds, is likely notzero. In fact, some shoreline managers use the levelof contributions from undeveloped watersheds as thebenchmark for water quality from developed water-sheds in the Los Angeles region (LARWQCB 2002).Unfortunately, the contributions of fecal indicatorbacteria from undeveloped watersheds to referencebeaches are largely unknown, which complicates this approach for assessing public health risk orbeach management.

The goal of this study was to assess the micro-bial water quality at reference beaches following wetweather events in southern California through meas-urements of fecal indicator bacteria. Referencebeaches were defined as those beaches located at themouth of undeveloped watersheds and whose bacter-ial contributions are minimally influenced by humanactivities. These data can then be used by publichealth agencies and beach managers for making

informed decisions about the reference condition ofmicrobial water quality during wet weather. Aseries of secondary objectives were also addressedduring this study to enhance our ability to decipherprocesses that can influence reference beach waterquality during wet weather. These objectivesincluded assessments of: 1) beach water qualityover time following rainfall to determine how longelevated concentrations of fecal indicator bacteriapersist; 2) the influence of storm size and seasonali-ty on beach water quality; 3) the relationshipbetween land-based inputs and microbial waterquality at reference beaches; 4) the relationshipbetween watershed size and microbial water quali-ty; and 5) the influence of lagoonal systems onmicrobial water quality at reference beaches.

METHODS

Six coastal reference beaches in southernCalifornia were selected for assessment of waterquality during wet weather. Reference beacheswere selected based on four criteria: 1) each refer-ence beach must be an open beach with breakingwaves; 2) each reference beach must have a fresh-water input; 3) the freshwater input must comefrom a watershed of similar size to nearby beachesthat receive wet weather inputs from urban water-sheds; and 4) the watershed discharging to the ref-erence beach must be >93% undeveloped.

The six reference beaches were: 1) Point MuguState Beach located at the mouth of Big SycamoreCreek in Ventura County; 2) Deer Creek Beachlocated at the mouth of Deer Creek in VenturaCounty; 3) Leo Carrillo State Beach located at themouth of Arroyo Sequit Creek in Los AngelesCounty; 4) Dan Blocker Beach located at the mouthof Solstice Creek in Los Angeles County; 5) SanOnofre State Beach located at the mouth of SanOnofre Creek in San Diego County; and 6) SanMateo Beach located at the mouth of San MateoCreek in San Diego County (Table 1; Figure 1). Allsix reference beaches are open with breaking wavesand have freshwater inputs. The six watersheds thatdischarge to these reference beaches range from 3 to346 km2, which is within the 25th and 75th interquar-tile range of watershed area for all of the watershedsthat drain to impacted, urbanized beaches in southernCalifornia. Five of the watersheds that drained tothe reference beaches were between 97% and 100%undeveloped, while one (San Mateo) was 93%developed, based on land use data compiled by the


US Geological Survey and University of CaliforniaSanta Barbara (Davis et al. 1998). Deer Creek wasthe smallest watershed and had the least amount ofhuman activity, while San Mateo Creek was thelargest watershed and had the greatest amount ofhuman activity.

Sampling The primary sampling location was in the ocean

immediately in front of the freshwater input at theso-called “wave wash” where the watershed dis-charge initially mixes with the ocean waves. Allsamples were collected between ankle and kneedepth on an incoming wave. The secondary sam-pling location was in the watershed discharge as itcrossed the beach at the closest sampleable locationprior to mixing with the ocean.

Samples at the primary samplingsites were measured for fecal indicatorbacteria and salinity. Samples at thesecondary sampling sites were measuredfor fecal indicator bacteria, salinity andflow. A subset of samples at secondarysites was collected for analysis ofhuman enteric virus to detect or rule outthe presence of human contributions offecal pollution. Samples were collectedin sterile 250-ml polystyrene bottles(bacterial analysis, salinity analysis) or4-L polyethylene carboys (enterovirusanalysis) following Standard Methods1060 protocol for aseptic sampling tech-niques (APHA 1995). Samples weretransported on ice to the laboratory foranalysis. Flow was measured using ahand held velocity meter (Marsh-

McBirney, Inc., Frederick, MD) and estimates ofwetted cross-sectional area.

Sampling focused on wet weather during the Falland Winter of 2004-2005 and 2005-2006. Wetweather sampling criteria included three or moredays of antecedent dry period and predicted mini-mum rainfall estimates of 0.10 inch. Four samplesper site were collected corresponding to the day ofthe storm (defined as within 24 of recorded rainfall)and the three days following recorded rainfall (fourdays of sampling in total). Storms were targetedbased on two factors: size of storm and seasonality.Size of storm was stratified into small storm events(less than mean daily rainfall) and large storm events(greater than mean daily rainfall) based on historicalrainfall at the nearest rain gage. Seasonality was


Reference Beach Watershed Latitude (NAD 83)

Longitude (NAD 83)

Watershed Size (km2)

Open Space (%)

BeachDirection

BeachSubstrate

Lagoonal System

Point Mugu Big Sycamore Creek

34 04.255 N 119 00.901 W 55.1 >95 SW Sand No

DeerCreek

DeerCreek

34° 03.724’ N 118° 59.164’ W

3.1 100 SW Sand No

LeoCarrillo

Arroyo Sequit

34° 2.671’ N 118° 55.950’ W

28.1 100 SW Sand Yes

Dan Blocker SolsticeCanyon

34° 01.970’ N 118° 44.539’ W

11.5 99 SW Sand and Cobble

No

SanOnofre

San Onofre Creek

33° 22.842’ N 117° 34.719’ W

110 97 W Sand and Cobble

Yes

San Mateo San Mateo Creek

33º 23.143’ N 117º 35.664’ W

346 93 W Sand and Cobble

Yes

Table 1. Reference beach and watershed characteristics.

0 25 50 75 100

Kilometers

-

Legend

Sampled Watersheds

Counties

Watersheds

Streams

!

!!

!

! Sampling Points

!

!

Figure 1. Map of reference beaches and watersheds.

stratified into early season (before December 31)and late season (after January 1) storm events.Storm season in southern California is defined asOctober 15 to April 15. To summarize, six refer-ence beaches were sampled over the course of fourdays during five different storm events for a total of120 sampling events.

Concentrations of total coliforms, E. coli, andenterococci were measured using kits supplied byIDEXX Laboratories, Inc. (Westbrook, ME).Concentrations of total coliforms and E. coli weremeasured using Colilert-18™ , while enterococciwas measured using Enterolert™. Samples were heat-sealed into Quanti-Tray/2000™ pouches incubatedovernight per the manufacturer’s instructions and sub-sequently inspected for positive wells (IDEXXWestbrook, MD). Conversion of positive wells to amost probable number (MPN) was done followingHurley and Roscoe (1983). Samples taken at BigSycamore Beach, Deer Creek Beach, Dan BlockerBeach, and Leo Carrillo State Beach were analyzed atthe City of Los Angeles laboratory facilities (ElSegundo, CA). Samples collected from San OnofreState Beach and San Mateo Beach were analyzed atWeston Solutions Laboratories (Carlsbad, CA).

All discharge samples from the first day of flowwere analyzed for human enterovirus. The purposeof this analysis was to eliminate human sewage as asource of indicator bacteria at each site. Sincethese viruses only infect and multiply in humansthrough the oral-fecal route, their detection is a reli-able marker for a human input of fecal contamina-tion into the system. During the 2005-2006 sam-pling period, all discharge samples were analyzedfor human enterovirus.

Following collection, water samples were passedthrough 0.45-μm pore size 47-mm Type HA filters(Millipore, New Bedford, MA) to concentrate virus-es. Volumes filtered ranged from 200 ml to 4 Ldepending on the turbidity and sediment load of theindividual sample. In some cases several filters wererequired to filter the entire sample. Filters wereimmediately stored at -80°C and subsequently trans-ported on dry ice to the University of SouthernCalifornia for human enterovirus analysis. Analysisfor the presence of human enterovirus was accom-plished following the method described by Fuhrman et al. (2005).

Data analysis focused on seven comparisons.The first compared the frequency of water qualitythreshold exceedences during wet weather to the fre-

quency of exceedences during winter dry weatherand summer dry weather. Wet weather was definedas the day of recorded rainfall plus the next threedays. Dry weather was defined as any day greaterthan three days since recorded rainfall. Winter wasdefined as November 1 to March 31, and summerwas defined as April 1 to October 31. Wet weatherdata were collected as part of this study. Winter andsummer dry weather data (April 2004 to March2005) for San Onofre State Beach were supplied bythe City of San Diego and the County of San Diego,respectively. Winter and summer dry weather data(April 2004 to March 2005) for Leo Carrillo StateBeach were supplied by the City of Los Angeles.Winter dry weather data (October 2004 to March2005) for Dan Blocker Beach were also supplied bythe City of Los Angeles; no summer dry weatherdata were available for this site. Winter and summerdry weather data (October 2003 through October2004) for Deer Creek Beach and Point Mugu Beachwere supplied by the Ventura County Department ofEnvironmental Health; winter dry weather data from2004-2005 at Deer Creek Beach and Point MuguBeach were not collected by the Ventura CountyDepartment of Environmental Health. Water qualitythresholds were based on single samples comparedto the California State Assembly Bill AB411 publichealth standards for marine bathing beaches: 1) >104enterococci / 100 ml; 2) >400 fecal coliform / 100 ml (E. coli were substituted for fecal coliform); 3) >10,000 total coliform / 100 ml; and 4) >1,000total coliform / 100 ml when the total coliform tofecal coliform (E. coli) ratio was <10.

The second data analysis element compared thefrequency of water quality exceedences among thefour days that comprised wet weather.Concentrations of fecal indicator bacteria for allbeaches combined were compared to California’swater quality thresholds within 24 hours of rainfalland three days following recorded rainfall.

The third data analysis element focused on compar-isons among the six reference beaches. The first com-parison examined the relative frequency of exceedenceof the state’s water quality thresholds for fecal indicatorbacteria for all storms combined. The second compari-son examined the magnitude of enterococci concentra-tions between the four days that comprised wet weath-er. Mean concentrations and standard deviations wereplotted against results within 24 hours of rainfall and upto three days following recorded rainfall. Enterococciwere chosen as the example indicator for this analysis.


The fourth data analysis element compared thefrequency of water quality exceedences betweensmall and large storms and between early and lateseason storms. Concentrations of fecal indicatorbacteria for all beaches combined were compared tothe state’s water quality thresholds for large andsmall storms as well as early and late season storms.A subsidiary data analysis examining storm biasquantified the frequency of water quality thresholdexceedences when storm flows generated watersheddischarges that did not cross the beach sand berm,when storm flows were large enough to breach thesand berm and for those watersheds that alwaysbreached the sand berm regardless of storm size.Deer Creek and Solstice Creek were two referencewatersheds that always breached the sand berm dur-ing this study. Big Sycamore, Arroyo Sequit, andSan Onofre Creeks had watershed discharges thatintermittently breached the sand berm.

The fifth data analysis element compared bacter-ial concentrations at each reference beach to salinitymeasurements and flux of bacteria into the surf zoneto evaluate the impact of watershed discharges onreference beach water quality. In this case, it wasassumed that salinity acted as a conservative tracerof freshwater inputs. Flux was calculated as theproduct of bacterial concentration and flow. For thisanalysis, data were only examined when watersheddischarges were entering the wave wash. Onceagain, enterococci was chosen for this analysis.

The sixth data analysis element examined theincidence of exceedences of California’s AB411water quality standards for fecal indicator bacteriacompared to the size of the watershed. Watersheds

were broken into small (<25 km2), medium (20 km2 -99 km2), and large (>100 km2), with two watershedsfalling into each category.

The last data analysis element focused on thepresence or absence of a lagoonal system. For pur-poses of this study a lagoon was defined as a persist-ent body of ponded water at the terminus of a creek.During most of the year, these lagoons are separatedfrom the ocean by a sand berm and only flow whenthe berm is breached by high volume flow from thecreek and/or by wave or tidal action. Here bacterialconcentrations in the wave wash were compared dur-ing wet weather when these systems were flowingversus when they were blocked by the sand berm.

RESULTS

Of the nine storm events sampled during thisstudy (Table 2), four occurred early in the season andfive occurred late in the season. Four of the stormswere larger (0.87 - 3.07 inches) and five were small-er (0.09 - 0.44 inches). Antecedent dry periodsranged from 2 to 34 days, depending upon the siteand storm event.

Genetic markers of human enterovirus weredetected in four discharge samples. These sampleswere collected from discharge across San OnofreState Beach on February 12, 2005; Leo Carillo StateBeach on March 3, 2006; and Dan Blocker beach onMarch 13 and 15, 2006. As is the case with the fecalindicator bacteria, the source of the virus particle(s)was unknown. Therefore, all data from these sam-pling events (wave wash and discharge) wereexcluded from the subsequent data analysis, as the


Storm Date Site

PointMugu2

DeerCreek1,2

LeoCarillo1,2

DanBlocker1

SanMateo3

SanOnofre3

10/27 - 10/30/04 - 1.31 1.31 1.31 - 3.07

12/5 - 12/8/04 - 0.41 0.41 0.41 - 0.39

1/29 - 2/1/04 - 0.44 0.44 0.44 - 0.16

2/12 - 2/15/05 - 2.04 2.04 2.04 - 2.44

10/18 - 10/21/05 1.02 - 1.02 - 1.97 -

1/1 - 1/4/06 1.94 - 1.94 - 0.87 -

2/19 - 2/22/06 0.8 - 0.8 - 0.31 -

2/28 - 3/3/06 1.87 - 1.87 - 0.87 -

3/12 - 3/15/06 0.11 0.11 0.11 0.09 0.35 0.35

1Malibu Big Rock rain gage 2Leo Carillo rain gage 3San Onofre rain gage

Table 2. Inches of precipitation during the nine storm events at the six reference beaches during the 2004-2005and 2005-2006 wet seasons.

detection of human enterovirus was indicative of thepossible presence of human fecal bacteria.

The prevalence of water quality exceedencescumulatively at the nine reference beach sites wasgreater during wet weather than during winter dryweather or summer dry weather, regardless of fecalindicator bacteria (Table 3). Approximately 16% ofall samples exceeded water quality thresholds for atleast one indicator during wet weather. This wasgreater than 10 times the frequency of water qualitythreshold exceedences during dry weather. Althoughthe frequency of water quality threshold exceedencesin wet weather was always greater than in winter dryweather or summer dry weather, the discrepancybetween time periods varied among the individualfecal indicator bacteria. For example, 12% of entero-cocci samples exceeded water quality thresholds dur-ing wet weather compared to 1% of the samples col-lected during winter dry weather and 0% of the sam-ples collected during summer dry weather.Comparatively, 10% of the samples analyzed for E. coli during wet weather exceeded water qualitythresholds compared to 1% of samples during winterdry weather and <1% during summer dry weather.

Water quality thresholds for total coliforms exceededwater quality thresholds <1% during dry weatherwhile total coliform to fecal coliform ratio onlyexceeded water quality thresholds during wet weather.

San Onofre State Beach and San Mateo Beachhad the greatest frequency of water quality thresholdexceedences during wet weather compared to theother four beaches sampled during this study (Table 3).Almost one-third of the samples at these sites exceed-ed water quality thresholds for at least one indicatorduring wet weather. Exceedences occurred at abouthalf this frequency in wet weather at Dan Blockerand Leo Carillo Beaches, with 15% and 17% of thesamples exceeding water quality thresholds for atleast one indicator during wet weather, respectively.In contrast, only 5% of samples exceeded thresholdsfor any indicator at Point Mugu State Beach duringwet weather, and no samples exceeded thresholds forany indicator at Deer Creek Beach.

The greatest frequency of water quality thresholdexceedences occurred within 24 hours post rainfallthen steadily decreased for three days (Figure 2).Twenty-seven percent of all samples collected <24 hours after rainfall exceeded water quality


Total E. coli Entero T:F Any

Wet 5.0 0 5.0 0 5.0

Winter Dry 0 0 0 0 0

Point Mugu State Beach

Summer Dry <1 0 0 0 <1

Wet 0 0 0 0 0


Deer Creek Beach

Summer Dry 0 0 0 0 0

Wet 5.6 8.3 11.1 8.3 16.7


Leo CarrilloState Beach


Wet 5.0 10.0 10.0 0 15.0

Winter Dry 0 2.8 1.4 0 2.8

Dan Blocker Beach


Wet 0 25.0 20.0 15.0 30.0

Winter Dry 10 10 10 0 20

San MateoState Beach

Summer Dry 1.9 1.9 9.3 0 9.3

Wet 25.0 15.0 25.0 0 30.0

Winter Dry 0 6.7 6.7 0 6.7

San Onofre State Beach


Wet 6.6 9.6 11.8 4.4 16.2

Winter Dry 0 1.4 1.4 0 1.4

All Beaches

Summer Dry <1 <1 <1 0 <1

Table 3. Frequency of water quality threshold exceedences for fecal indicator bacteria (total coliform (Total), E. coli (E. coli), enterococci (Entero), total coliform E. coli ratio (T:F), and any indicator(Any)) expressed as a per-cent of days during wet weather (<3 days after rainfall, this study), winter dry weather (>3 days after rainfall,November to March) and summer dry weather (>3 days after rainfall, April to October) at the reference beachestargeted during this study.

thresholds for at least one indicator. This frequencyof water quality threshold exceedences decreased to21% of samples collected one day following record-ed rainfall, then 15% of samples collect for two daysfollowing recorded rainfall and ultimately declinedto 3% of samples three days following recorded rain-fall. This pattern of water quality threshold excee-dences was repeated by virtually every bacterial indicator, but at varying levels of frequency.Enterococci exhibited the greatest rate of waterthreshold exceedences <24 hours following recordedrainfall and the greatest persistence 3 days followinga rain event. For example, the frequency of waterquality threshold exceedences for total coliform, E. coli, enterococci, and total coliform:fecal coliformratio within 24 hours of recorded rainfall was 13%,20%, 20%, and 7% of all samples, respectively, butenterococci was responsible for all of the excee-dences recorded on day 3.

Exceedences of water quality thresholds for fecalindicator bacteria in wet weather occurred more thantwice as frequently in large (>100 km2) watershedsthan in medium (25 – 100 km2) watersheds, andmore than four times as frequently than in small(<25 km2) watersheds (Figure 3). More than anyother indicator, concentrations of enterococci wereresponsible for the majority of water quality thresh-old exceedences across all three watershed size cate-gories, exceeding thresholds 22% of the time forlarge-sized watersheds, 9% for medium-sized water-sheds and 5% for small-sized watersheds. Although

total coliform and E. coli concentrations did notexceed water quality thresholds as often as did ente-rococci, they followed the same trend in terms ofwatershed size.

Early season storms resulted in a greater numberof water quality threshold exceedences than late sea-son storms (Figure 4A). After combining all wetweather samples at all creeks, 18% of the samplesfrom early season storms exceeded water qualitythresholds for at least one indicator, while 15% ofthe samples from late season storms exceeded waterquality thresholds for at least one indicator. Earlyseason storms also had a greater frequency ofexceedence of more than one threshold compared tolate season storms. In fact, 63% of the samples thatexceeded water quality thresholds during early sea-son storms exceeded more than one threshold (i.e.,E. coli and enterococci). In contrast, 67% of thesamples that exceeded water quality thresholds dur-ing late season storms exceeded only one threshold.

Larger storms resulted in a greater number ofwater quality threshold exceedences than smallstorms (Figure 4B). After combining all wetweather samples at all creeks, 21% exceeded waterquality thresholds for at least one indicator duringlarge storms, compared to 12% of wet weather sam-ples in small storms. This discrepancy betweenlarge and small storms was similar, or greater, fortotal coliform and enterococci thresholds andslightly lower for E. coli. For example, 16% of theenterococci samples exceeded water quality thresh-


Total E coli Entero T:F Any

Exc

eedance

s (%

)

0

5

10

15

20

25

30

Figure 2. Frequency of water quality threshold excee-dences for fecal indicator bacteria: total coliform(Total), E. coli (E. coli), enterococci (Entero), total coliform E. coli ratio (T:F) and any threshold (Any) -within 24 hours of rainfall, and 3 subsequent days, at6 reference beaches used in this study during the2004-2005 and 2005-2006 storm seasons.


Exc

eedance

s (%

)

0

5

10

15

20

25

30

35

Large Watershed Medium Watershed Small Watershed

Figure 3. Comparison of water quality thresholdexceedences: total coliform (Total), E. coli (E. coli),enterococci (Entero), total coliform E. coli ratio (T:F)and any threshold (Any) - in the wave wash with water-shed size (small: 3 - 12 km2; medium: 28 - 56 km2; andlarge: 110 - 346 km2).

olds following large-sized rainfall events comparedto 7% during smaller-sized rainfall events. In con-trast, the total coliform-to-fecal coliform ratioexhibited the opposite trend, exceeding water quali-ty thresholds only 1% of the time after large stormsand 7% after small storms.

One factor that accounted for the differences inwater quality threshold exceedences observedbetween large and small-sized rainfall events was theability of stormwater flow to breach the sand bermand discharge across the reference beach (Figure 5).Storms that were capable of producing sufficientflows to breach the sand berm were more than fourtimes more likely to exceed water quality thresholdswhen flowing to the ocean compared to storms

where the sand berm blocked flow across the beach.For example, almost 40% of the wet weather sam-ples exceeded water quality thresholds for at leastone indicator when the sand berm was breachedcompared to 12% of the samples when it had notbreached. Similar patterns of threshold exceedencefrequency were observed for each of the individualbacterial indicators.

At reference beaches that always breached thesand berm, the frequency of water quality thresholdexceedences was low and similar to when the sandberm had not been breached at the more intermittentbeaches (Figure 5). For example, 8% of the entero-cocci samples exceeded water quality thresholds atboth sites where the berm was always breached (i.e.,Deer Creek and Dan Blocker Beaches) and at siteswhen the berm was not breached (i.e., Leo Carrilloand San Onofre State Beaches). Beaches that alwaysbreached were characterized as relatively small andlacked lagoon systems at their termini.

Discharges from reference watersheds appearedto be the predominant source of fecal indicator bac-teria at reference beaches during wet weather. Forexample, concentrations of enterococci in the wavewash were positively correlated to the flux of entero-cocci in the discharge from their respective undevel-oped watersheds (Figure 6). The flux of enterococcifrom undeveloped watersheds could explain roughly73% of the variation observed in concentrations of



Exc

eedance

s (%

)

0

2

4

6

8

10

12

14

16

18

20


Exc

eeda

nce

s (%

)

0

5

10

15

20

25

A)

B)

Early Late

Large Small

Figure 4. Comparison of water quality thresholdexceedences within <3 days post rainfall at four refer-ence beaches used in this study for fecal indicatorbacteria - total coliform (Total), E. coli (E. coli), entero-cocci (Entero), total coliform E. coli ratio (T:F) and any threshold (Any), during: large and small stormevents during the 2004-2005 and 2005-2006 storm sea-sons (A); and early and late season storms during the2004-2005 and 2005-2006 storm seasons (B).


Exc

eedance

s (%

)

0

10

20

30

40

50

Breached Blocked Always Flowing

Figure 5. Comparison of water quality thresholdexceedences for fecal indicator bacteri: total col-iform (Total), E. coli (E. coli), enterococci (Entero),total coliform E. coli ratio (T:F) and any threshold(Any), at reference beaches when creeks with andwithout lagoonal systems from undeveloped water-sheds behind the beach are always flowing, blockedbehind a sand berm, and when the sand berm is tem-porarily breached.

this indicator in the wave wash (r2 = 0.73). Thissuggests that land-based sources are likely the majorcontributor to concentrations of enterococci in thewave wash when creeks are flowing.

In cases where there was a terminal lagoon, thewatershed, not the lagoon, appeared to be the pre-dominant source of fecal indicator bacteria whensand berms were breached (Figure 7). The relation-ship between concentrations of fecal indicator bacte-ria in the discharge across the beach and in the creekabove the lagoon was near unity. Concentrations inthe creek could explain between 93% and 99% of thevariability for each of the three fecal indicator bacte-ria in the discharge (r2 = 0.99, 0.99, and 0.93 forenterococci, E. coli, and total coliform, respectively).The relationship between concentrations of fecalindicator bacteria in the discharge across the refer-ence beach and in the lagoon above the beach wasalso near unity. Concentrations in the lagoon couldexplain between 96% and 99% of the variability foreach of the three fecal indicator bacteria in the dis-charge (r2 = 0.98, 0.99, and 0.96 for enterococci, E.coli, and total coliform, respectively). The similarityof fecal indicator bacteria concentrations between thecreek, the lagoon, and the discharge demonstratedthat the lagoon had little influence on inputs to theocean and was essentially a conduit for the creekduring wet weather.

It appears that factors other than flow may beresponsible for water quality exceedences at refer-ence beaches with intact sand berms when storms areinsufficient to breach berms (Table 4). For example,San Mateo Creek never breached its sand berm dur-ing the sampling period, yet this reference beach hada similar frequency of bacterial water quality thresh-old exceedences as those of adjacent San OnofreCreek when its sand berm was breached. A possiblereason for the large number of exceedences at thisnon-breached site was the large number of WesternGulls observed feeding on the beach during wetweather sampling (Table 4).

DISCUSSION

This study demonstrated that natural contribu-tions of fecal indicator bacteria at beaches with mini-mal or no known human impacts were sufficient togenerate exceedences of the State of California waterquality thresholds during wet weather. On average,one-fifth of all samples collected within three daysof rainfall exceeded water quality thresholds for atleast one bacterial indicator and these exceedences


Flux (cells/minute) Enterococci in Discharge

104 105 106 107 108 109 1010

En

tero

cocc

i (M

PN

/10

0m

l) in

Wa

vew

ash

100

101

102

103

104

Figure 6. Enterococci concentrations in the wavewash compared to flux from undeveloped watershedsdischarging to reference beaches.

Beach Discharge (Enterococci/100 mL)

100 101 102 103 104 105

Cre

ek o

r La

goon

(E

nter

ococ

cus/

100

mL)

100

101

102

103

104

105

Discharge vs Lagoon Discharge vs Creek

Beach Discharge (E. coli /100 mL)

101 102 103 104

Lago

on o

r C

reek

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ecal

Col

iform

/100

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101

102

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Beach Discharge (Total Coliform /100 mL)

103 104 105 106

Lago

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otal

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106


Figure 7. Comparison of fecal indicator bacteria con-centration in the discharge across San Onofre Beachto concentrations in San Onofre Creek and in the San Onofre Lagoon.

were observed at three-quarters of the referencebeaches sampled. Concentrations of enterococci andE. coli led to exceedences of water quality thresholdsmost frequently, while total coliform and total col-iform to fecal coliform ratios led to the least numberof exceedences.

Wet weather discharges from undevelopedwatersheds generally contributed to higher concen-trations of fecal indicator bacteria along referencebeaches relative to other times of the year.Moreover, concentrations of fecal indicator bacteriaat the reference beaches were positively correlatedwith flux of indicator bacteria in the discharge drain-ing from the undeveloped watersheds. Similar tothis study, Schiff and Kinney (2001) found a largequantity of fecal indicator bacteria in wet weatherdischarges in similar-sized, almost entirely undevel-oped watersheds, from inland San Diego Countywith no human activity.

There were a number of interrelated factors thatappeared to affect the flux of indicator bacteria fromundeveloped watersheds and the resulting frequencyof water quality threshold exceedences at referencebeaches during wet weather. These included water-shed size, storm size, and early vs. late seasonstorms. Watershed size and storm size relate to afunction of source strength and transport. Largerwatersheds and larger storms both have the capabili-ty to generate and mobilize more bacteria from with-in the watershed. In fact, the largest storms at thelargest watersheds generated the greatest frequencyof water quality exceedences and for multiple fecalindicator bacteria thresholds. Smaller storms and/orsmaller watersheds generated lesser bacterial fluxand fewer beach exceedences resulted.

A third interrelated factor was the presence of alagoon and sand berm at the terminus of the creek.A significant increase in the frequency of water qual-ity exceedences occurred when stable beach bermsblocking lagoons were breached. This occurred con-

sistently during large storm events. Conversely,when rainstorms were insufficiently large to breakthrough stable beach berms, the frequency of waterquality exceedences was reduced. The presence ofthe lagoon appeared to have little effect on the fluxof indicator bacteria when the sand berm wasbreached; concentrations in the discharge across thebeach were nearly identical to concentrations in thecreek above the lagoon. However, the presence ofthe lagoon did appear to have an effect on waterquality threshold exceedences at reference beacheswhen storms were insufficiently large to breach sandberms. In this case, an increase in seabirds roostingon the beach near lagoons was observed with a con-comitant increase in water quality exceedences.Birds have been implicated in bacterial water qualityexceedences at other locations (Choi et al. 2003,Abulreesh et al. 2004) and it is possible, at least inthis case, the freshwater lagoon (particularly at SanMateo Creek) acts as a bird attractant, drawing birdsto the beach where their dropping may be resuspend-ed by wave and tidal action.

Seasonality also affected reference beachmicrobial water quality. Early season storms had agreater frequency of water quality exceedencescompared to late season storm events, possiblyfrom a “first flush” effect, which may have servedto bring large amounts of accumulated debris andassociated bacteria down to the beach from theupper reaches of watersheds. Additionally, earlyseason storms had a greater magnitude of waterquality threshold exceedences, exceeding by morethan one indicator in the majority of the wet weath-er samples collected. In contrast, the vast majorityof water quality exceedences in late season stormsexceeded for only a single indicator.

One last factor affecting the ability to adequatelyassess factors contributing to concentrations of fecalindicator bacteria at references beaches is the detec-tion of human enterovirus markers at San Onofre and


LagoonBreached

LagoonNot Breached

#Exceedences

Average # Birds

# Exceedences

Average # Birds

Leo Carillo 4 24 - -

San Onofre 4 <1 1 0

San Mateo - - 4 131

Table 4. Average number of birds observed when water quality standards for enterococci were exceeded andlagoon was breached versus not breached.

Leo Carillo State Beaches and at Dan BlockerBeach. Although virus was detected during a verysmall number of events that were excluded from ourcalculations, it cast a shadow of doubt regardinghuman contributions over the remaining storms ateach of these sites. All of these watersheds werecharacterized by very little development (<3%), butvirtually all of the watersheds in our study havesome human trespass. Ultimately, insufficient dataemerged to eliminate all results from these water-sheds; consequently, the watersheds were included.Hence, a complete accounting of sources at thesesites may not be forthcoming until more accuratetechnology for identifying and quantifying fecalsources is available.

The human health risk associated with wetweather discharges of nonhuman sources of fecalindicator bacteria is not known. Several epidemiolo-gy studies have examined the effect of increasedfecal indicator bacteria on the risk of swimming-related illnesses (See Wade et al. 2003 for a review).Cabelli et al. (1982) found a relationship betweenenterococci and health effects at a marine bathingbeach in New Jersey, but this beach was impacted byknown point sources of human fecal pollution. Haileet al. (1999) found a relationship between indicatorbacteria concentrations and health effects in thosewho swam near storm drains in Santa Monica Bay,but these drains were also known to contain humansources of fecal pollution. Colford et al. (2007)found no relationship between largely nonhumansources of fecal indicator bacteria or genetic markersfor human enterovirus and health effects in MissionBay, a marine bathing beach in San Diego, but onlyexamined dry weather. In this study, nonhumansources of bacteria from nonpoint sources during wetweather were quantified; however, health risks werenot examined. Epidemiological studies during wetweather need to be conducted in order to estimatethe risk of swimming related illnesses at referencebeaches like those examined in this study.

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ACKNOWLEDGEMENTS

We wish to acknowledge Stan Asato andIoannice Lee at the City of Los Angeles, and LarissaAumand and Rosabel Diaz at Weston Solutions, Inc.,for their assistance with sample collection and analy-sis. Thanks also to Renee DeShazo, our projectmanager at the Los Angeles Regional Water QualityControl Board. Funding for this study was providedby the Regional Water Quality Control Board- LosAngeles District.


microbiological water quality at non-human influenced reference beaches in southern california...

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